Computational cost is a major concern in practice. Table REF  compares several state-of-the-art neural networks in terms of model size and real time factor (RTF). The RTF is calculated on an Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz with input samples of 10sec. As shown in Table REF , state-of-the-art speaker recognition models like SE-ResNet34[1]} and ECAPA-TDNN[2]} are computationally intensive. The most recent Transformer-based modelshttps://huggingface.co/microsoft/wavlm-base-svhttps://huggingface.co/microsoft/wavlm-large trained by SSL[3]}, [4]}, [5]} are much heavier than the aforementioned. In comparison, the classical x-vector extractor[6]} is applicable due to its relatively small model size and low RTF. The light-weight modelhttps://github.com/pytorch/vision/blob/main/torchvision/models, MobileNet3[7]} is employed from image classification to speaker recognition. It is more light-weight than the x-vector extractor.
